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International Journal of Web Based Communities (2 papers in press)

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Racism and Social Media: A Study in Indian Contextby Naganna Chetty, Sreejith Alathur Abstract: Racism is a kind of hatred behaviour, exhibited in written, verbal or physical form against the ethnicity or physical appearance of a group or an individual. Around the world, problematic behavioural incidents occur and in India, media often interpret it as towards people of colour or blacks. There was a lot of discussion about these incidents both online and offline, some groups consider that India is not free from racism and others view it is of misinformation processing. Therefore, there is a requirement of a system to evaluate and determine the possible scenarios towards racism in India. In this regard, using four different sets of keywords we created Twitter datasets. The data collected from social media are analysed to identify the polarity of content and the amount of racism using the software developed in R programming language. Contents are categorised in different polarities such as racist, non-racist and neutral.Keywords: Racism Hate Twitter Black African Bollywood India.DOI: 10.1504/IJWBC.2019.10017787

Lexicon Based Twitter Sentiment Analysis for Vote Share Prediction Using Emoji and N-gram Featuresby Barkha Bansal, Sangeet Srivastava Abstract: Recently, Twitter sentiment analysis (TSA) has been successfully employed to monitor and forecast elections in many studies. However, most of the existing studies rely on extracting sentiments from explicit textual features. Moreover, only few studies have included non-textual features such as emojis for election forecasts. In this study, we incorporated N-gram features to predict vote shares of 2017 Uttar Pradesh (UP) legislative elections. Also, sentiment distribution of tweets containing emojis was significantly different from tweets without emojis. Therefore, emoji sentiments were detected and incorporated to predict the vote shares. We collected more than 0.3 million tweets, wherein geo-tagging was applied on search keywords that were not exclusive to elections. We employed seven lexicons for labelling tweets and compared two methods to reduce prediction error: sentiment magnitude-based criteria and polarity of tweets. Results show that proposed method of incorporating N-gram features and emoji sentiments significantly decreases prediction error.Keywords: Twitter Sentiment Analysis; Emoji; Geo-tag; Electoral Forecast; Lexicon; N grams.DOI: 10.1504/IJWBC.2019.10018048